Research Interests

My background in computer science, with special emphasis on information technology, and in quantitative fields such as data science, optimization and statistics makes my interests focus on the intersection between the use of different sensor systems and the investigation of data-driven models to solve different challenges. In this line, I can list some of my research topics of interest: 

• Development of Internet of Things Systems

• Analysis of Sensor-Based Systems

• Development of Novel Machine Learning Approaches to Sensor Networks

• Data-Driven Techniques for Ensuring Sensor Data Quality

• Air Quality Sensor Networks

• Development of Data-Driven Low-Cost Sensor Calibration Techniques

• Data-Driven Techniques for Internet of Things Systems

•  Supervised Machine Learning Techniques (e.g., SVM, SVR, KNN, MLR)

• Unsupervised Machine Learning Techniques (e.g., PCA, PLS, KPCA, SOM, t-SNE)

• Anomaly Detection (e.g., PCA, KNN, LoF, VAE)

• Kernel Methods (e.g., SVR, KRR, KPCA)

• Sparse Signal Reconstruction Models

• Graph Theory

• Artificial Neural Networks (e.g., LSTM, VAE, CNN, GNN)

•  Interface IoT platforms and digital twins.

• Optimization of IoT platforms for digital twin applications.

• Digital twin applications in industrial environments.